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Blockchain technology uses a consensus mechanism to create and finalize blocks. The consensus mechanism affects the total performance parameters of the blockchain network, such as throughput. In this paper, we present “Nazfast”, a simplified proof of stake—Byzantine fault tolerance based consensus mechanism to create and finalize blocks. The presented consensus is completed in multiple folds. For block producer and validation committee selection, we used a secure and speeded-up election mechanism, S&Sem, in Nazfast. The consensus is designed for fast block finalization in a malicious environment. The simulation result shows that we approximately achieved three block finalizations in 1 s with almost similar latency. We reduced and fixed the number of validators in the consensus to improve the throughput. We achieved a higher throughput among other consensus of the same family. Because we reduced the number of validators, the safety parameters of the consensus are at risk, so we used Sea Shield to improve the overall consensus safety. This is another blockchain to save nodes’ details when they join/unjoin the network as validators. By using all three parts together, our system is protected from 28-plus different attacks, and we maintain a high decentralization by using S&Sem. Finally, we also enhance the incentive mechanism of consensus to improve the liveness of the network.

PurposeThis study aims to examine the relationship between green human resource management (GHRM) practices and green transformational leadership toward inducing employees' green creativity. Specifically, drawing upon the ability, motivation and opportunity theory, the authors tested how green perceived organizational support (green POS) mediates the link between GHRM practices and employees' green creativity. Furthermore, based on the firm's resource-based view, the authors examine the moderating role of green transformational leadership on the relationship between GHRM practice and green POS.Design/methodology/approachUsing a survey questionnaire, this research was conducted with a multi-source sample of 201 supervisors and their 428 subordinates from organizations working in grocery, food and personal care products in Pakistan.FindingsThe findings of structural equation modeling revealed that green POS plays a mediating role between GHRM and employees' green creativity. The study findings also highlighted that green transformational leadership moderates the positive relationship between GHRM practices and green POS.Practical implicationsOrganizations need to implement GHRM practices to achieve environmental performance. Individuals are likely to recognize themselves with organizations that are engaged in green practices, and therefore, organizations can get benefits from implementing GHRM practices.Originality/valueThis research explores green POS and green transformational leadership as novel mechanisms through which GHRM practices influence employees' green creativity in organizations. In addition, the authors empirically examined our theorized relationships in the South Asian context.

To be a stakeholder/validator/token holder is not so difficult in the Proof of Stake (POS)-based blockchain networks; that is why the number of validators is large in these networks. These validators play an essential part in the block creation process in the PoS-based blockchain network. Due to the large validators, the block creation time and communication message broadcasting overhead get increased in the network. Many consensus algorithms use different techniques to reduce the number of validators, such as Delegated Proof of Stake (DPoS) consensus algorithms, which select the set of delegators via stake transactions for the block creation process. In this paper, we propose S&SEM, a secure and speed-up election process to select the ‘z’ number of validators/delegators. The presented election process is based on a traditional voting style with multiple numbers of rounds. The presented election mechanism reduces the possibility of malicious activity in the voting process by introducing a special vote message and a round that checks duplicate votes. We did horizontal scaling in the network to speed up the election process. We designed an improved incentive mechanism for the fairness of the election process. The designed reward and penalty procedure controls the nodes’ behaviors in the network. We simulate the S&SEM, and the result shows that the presented election process is faster and more secure to select delegators than the existing process used by DPOS.

Part-of-speech (POS) and dependency grammar (DG) are the basic components of natural language processing. However, current word vector models have not made full use of both POS information and DG information, and hence the models’ performances are limited to some extent. The authors first put forward the concept of POS vector, and then, based on continuous bag-of-words (CBOW), constructed four models: CBOW + P, CBOW + PW, CBOW + G, and CBOW + G + P to incorporate POS information and DG information into word vectors. The CBOW + P and CBOW + PW models are based on POS tagging, the CBOW + G model is based on DG parsing, and the CBOW + G + P model is based on POS tagging and DG parsing. POS information is integrated into the training process of word vectors through the POS vector to solve the problem of the POS similarity being difficult to measure. The POS vector correlation coefficient and distance weighting function are used to train the POS vector as well as the word vector. DG information is used to correct the information loss caused by fixed context windows. Dependency relations weight is used to measure the difference of dependency relations. Experiments demonstrated the superior performance of their models while the time complexity is still kept the same as the base model of CBOW.

The study aims to explain how marketing variables (quality, value, satisfaction) combined with corporate image can explain the loyalty and word-of-mouth of those attending a sporting event. It also aims to know the different user profiles in the event and how these variables interact in each of these profiles. For this purpose, 697 sporting event attendees were surveyed. Structural model analysis was combined with unobserved a posteriori segmentation (POS) through PLS, which allows us to know the groups without a prior criterion. The results confirmed the hypothesis, explaining loyalty and word-of-mouth in a sporting event and revealing three unobserved groups of fans: involved, nonconforming, and opportunistic. The proposed model is useful to explain loyalty and word-of-mouth and the segments of users. On the other hand, corporate image must be considered to understand consumer behavior in sporting events, because it has shown influence, especially in the involved and opportunistic segments.

Protected areas are a cornerstone for forest protection, but they are not always effective during times of socioeconomic and institutional crises. The Carpathian Mountains in Eastern Europe are an ecologically outstanding region, with widespread seminatural and old-growth forest. Since 1990, Carpathian countries (Czech Republic, Hungary, Poland, Romania, Slovakia, and Ukraine) have experienced economic hardship and institutional changes, including the breakdown of socialism, European Union accession, and a rapid expansion of protected areas. The question is how protected-area effectiveness has varied during these times across the Carpathians given these changes. We analyzed a satellite-based data set of forest disturbance (i.e., forest loss due to harvesting or natural disturbances) from 1985 to 2010 and used matching statistics and a fixed-effects estimator to quantify the effect of protection on forest disturbance. Protected areas in the Czech Republic, Slovakia, and the Ukraine had significantly less deforestation inside protected areas than outside in some periods; the likelihood of disturbance was reduced by 1-5%. The effectiveness of protection increased over time in these countries, whereas the opposite was true in Romania. Older protected areas were most effective in Romania and Hungary, but newer protected areas were more effective in Czech Republic, and Poland. Strict protection (International Union for Conservation of Nature [IUCN] protection category Ia-II) was not more effective than landscape-level protection (IUCN III-VI). We suggest that the strength of institutions, the differences in forest privatization, forest management, prior distribution of protected areas, and when countries joined the European Union may provide explanations for the strikingly heterogeneous effectiveness patterns among countries. Our results highlight how different the effects of protected areas can be at broad scales, indicating that the effectiveness of protected areas is transitory over time and space and suggesting that generalizations about the effectiveness of protected areas can be misleading. Las áreas protegidas son una piedra angular para la protección de los bosques, pero no son siempre efectivas durante los momentos de crisis socioeconómica e institucional. Las montañas de los Cárpatos en Europa Oriental son una región sobresaliente ecológicamente, con bosques semi-naturales extensos y bosques de viejo crecimiento. Desde 1990, los países de los Cárpatos (República Checa, Hungría, Polonia, Rumania, Eslovaquia y Ucrania) han experimentado dificultades económicas y cambios institucionales, incluyendo la caída del socialismo, el ascenso de la Unión Europea y una rápida expansión de las áreas protegidas. La pregunta es cómo ha variado la efectividad de las áreas protegidas durante estos momentos a través de los Cárpatos dados estos cambios. Analizamos un conjunto de datos satelitales sobre la perturbación del bosque (es decir, la pérdida de bosque a causa de la cosecha o las perturbaciones naturales) desde 1985 a 2010 y utilizamos estadístics correspondiente y un estimador de efectos fijados para cuantificar el efecto de la protección sobre la perturbación del bosque. Las áreas protegidas en la República Checa, Eslovaquia y Ucrania significativamente tuvieron menor deforestación dentro que afuera; la probabilidad de perturbación fue reducida en un 1 - 5 %. La efectividad de la protección incrementó con el tiempo en estos países, mientras que lo contrario fue cierto para Rumania. Las áreas protegidas más viejas fueron más efectivas en Rumania y Hungría, pero las más nuevas fueron más efectivas en la República Checa y Polonia. La protección estricta (categoría Ia-II de la Unión Internacional para la Conservación de la Naturaleza [UICN]) no fue más efectiva que la protección a nivel de paisaje (IUCN III-IV). Sugerimos que la fuerza de las instituciones, las diferencias en la privatización de los bosques, el manejo de los bosques, la perturbación previa de las áreas protegidas y cuando los países se unen a la Unión Europea pueden proporcionar explicaciones para los patrones impresionantemente heterogéneos de efectividad entre los países. Nuestros resultados resaltan cómo pueden ser los diferentes efectos de las áreas protegidas a escalas generales, indicando que la efectividad de las áreas protegidas es transitoria a lo largo del tiempo y el espacio, y sugiriendo que las generalizaciones sobre la efectividad de las áreas protegidas pueden ser engañosas.

Drawing on social exchange theory, the purpose of this study is to examine the mediating role of psychological empowerment and moderating role of proactive personality in the relationship between POS and job satisfaction. The data were collected from 936 employees working in various manufacturing and service sectors by using self-report survey questionnaires by employing time-lagged cross-sectional study design. The study findings demonstrate that POS positively influenced psychological empowerment and job satisfaction. Moreover, it is also revealed that the relationship between POS and job satisfaction is weaker when employees' proactive personality is higher rather than lower. The findings of the current study pose a framework for organizational representatives of both service and manufacturing industries to strengthen individual psychological empowerment and job satisfaction by offering organizational support to those individuals who are less proactive.

Although the idea of communication between devices is not new, its developmenthas been rapid and significant since it helps people do their jobs more efficiently and keeps them fully informed of events at their homes and workplaces thanks to technology like the Blockchain (BC) based Internet of Things (IoT). However, this new technology suers from security issues and the existing research has not addressed these issues in depth. In this paper, a simulation of the smart network of the firefighting station was made. BC technology was used with one of the consensus algorithms which was proof of authority (PoA) to make this network more secure and private, in addition to the use of a hash function such as secure hash algorithm 384 (SHA-384), which is a one-way encryption function and was used to verify the BC data integrity so that it was dicult to hack the data and thus be the data transmission process is more secure. Also, the Espressif 32 (ESP32) device was chosen for this project because it oers several useful characteristics, including Wi-Fi and the capacity for rapid data transmission. It was observed from the results obtained from the application of consensus algorithms that the firefighting station network was made more secure and the PoA algorithm was better in several aspects such as execution time (maximum 0.4 S) and memory used (maximum 610 KB). Finally, the proposed work that was applied to the firefighting station was good in terms of safety and privacy, as the work of this station became more efficient.

The success of clustering or classification methods the detection of relevant textual formats is incredibly meaningful. The high dimensionality and irrelevance of textual materials was subjected to text records. Existing methods lack integration and are particularly vulnerable to original value. Metaheuristic algorithms are also applied to solve the challenges of standard classification algorithms. In this paper, an enhanced Latent Dirichlet Assignment clustering method & Inter Modeling for Tag Suggestion rating system is documented to boost correlation - based & identification efficiency to suggest labels with material modern web labels that promotes the exchange of medical information using unmonitored data through question-answering. For accurate tagging, Methods like POS marking, Hopping, Whistles& Stopping words are being used for speech recognition. The efficiency of the evolved architectures is compared to the standard methods, by using specificity of the recommendation, defining features, sensitivity, plain word and speed. The findings reveal that the classification and grouping scheme of the proposed structure succeeds traditional textual record approaches.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.